社区安全风险评估和影响因素分析的算法通过反向传播神经网络进行分析
Shuang Zhou1, Meiling Du1, XiaoYu Liu2
1School of Public Administration, Tianjin University of Commerce, Tianjin, 300134, China.
Heliyon
|May 9, 2024
概括
这项研究优化了社区安全风险评估,使用反向传播神经网络 (BPNN),在识别威胁方面达到99.1%的准确性. 增强的BPNN模型在社区安全风险管理的准确性和效率方面明显优于传统方法.
科学领域:
- 计算机科学,人工智能,机器学习
- 公共管理,城市研究,社区管理
背景情况:
- 传统的社区安全风险评估模型与高维数据和复杂的特征交互作斗争.
- 准确识别和预测各种威胁 (自然灾害,健康危机,社会问题) 对社区安全至关重要.
- 现有的方法在有效管理社区环境和风险因素的动态性质方面面临挑战.
研究的目的:
- 使用反向传播神经网络 (BPNN) 优化社区安全风险评估模型.
- 提高识别和管理各种社区安全风险的准确性和效率.
- 克服传统模型在处理大数据集和复杂的风险因素相互作用方面的局限性.
主要方法:
- 开发和优化用于安全风险评估的反向传播神经网络 (BPNN) 模型.
- 建立基于影响因素的安全风险评估指标的综合系统.
- 与传统模型进行比较分析,包括CNN,LSTM,BERT,GPT和XGBOOST.
主要成果:
- 优化的BPNN模型通过20个隐藏层节点实现了99.1%的最终识别精度.
- 该模型显示出高于平均准确度 (98.5%) 和比传统方法提高了9% -11%.
- 优化模型的功能损失显著降低,响应时间更快 (100-120毫秒),资源利用率降低.
结论:
- 优化的BPNN模型为社区安全风险评估提供了高度准确和高效的解决方案.
- 这项研究为社区安全风险形成机制和模式提供了新的见解.
- 根据实验结果提出了解决社区安全风险的建议和策略.
相关概念视频
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
125
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
125
01:18Steps in Outbreak Investigation
123
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
123